A patient-specific airflow simulation was developed to help address the pressing need for an expansion of the ventilator capacity in response to the COVID-19 pandemic. The computational model provides guidance regarding how to split a ventilator between two or more patients with differing respiratory physiologies. To address the need for fast deployment and identification of optimal patient-specific tuning, there was a need to simulate hundreds of millions of different clinically relevant parameter combinations in a short time. This task, driven by the dire circumstances, presented unique computational and research challenges. We present here the guiding principles and lessons learned as to how a large-scale and robust cloud instance was designed and deployed within 24 hours and 800 000 compute hours were utilized in a 72-hour period. We discuss the design choices to enable a quick turnaround of the model, execute the simulation, and create an intuitive and interactive interface.

中文翻译：

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COVID19 的云计算：从大规模并行呼吸机拆分模型中吸取的经验教训

开发了针对患者的气流模拟，以帮助解决扩大呼吸机容量以应对 COVID-19 大流行的迫切需求。该计算模型提供了有关如何在两名或多名呼吸生理不同的患者之间拆分呼吸机的指导。为了满足快速部署和识别最佳患者特异性调整的需求，需要在短时间内模拟数亿种不同的临床相关参数组合。在严峻环境的推动下，这项任务提出了独特的计算和研究挑战。我们在此介绍了有关如何在 24 小时内设计和部署大规模且强大的云实例以及如何在 72 小时内使用 800 000 个计算小时的指导原则和经验教训。